P255 Quantifying Negative Feedback Inhibition in Epilepsy to Assess Excitability
Thomas J Richner1, Nicholas Gregg1, Raunak Singh1, Keith Starnes1, Dora Hermes2, Jamie J Van Gompel3, Gregory A Worrell1,Brian N Lundstrom*1
1. Department of Neurology, Mayo Clinic, Rochester, MN, USA
2. Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
3. Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
*Email:lundstrom.brian@mayo.edu
Introduction
Normal cortical function depends on precisely regulated excitability, which is controlled by a balance of excitation and negative feedback inhibition. Negative feedback inhibition mechanisms, such as spike frequency adaptation (SFA) and short-term synaptic depression (STD), act over multiple timescales to reduce excitability. However, negative feedback inhibition is often difficult to quantify and neglected in neuroscience experiments. We’ve developed a framework for quantifying multiple timescale negative feedback inhibition and are applying it to epilepsy patients undergoing invasive EEG epilepsy monitoring. We also modeled negative feedback inhibition to understand how SFA and STD affect EEG signals.
Methods
Novel electrical stimulation waveforms were delivered to epilepsy patients undergoing stereotactic EEG monitoring. Sinusoidally modulated pulse trains were delivered to cortical sites, varying the envelope period between 2 and 10 seconds (5 Hz carrier frequency). Cortico-cortical evoked potentials (CCEPs) were recorded from nearby electrodes. Negative feedback inhibition was assessed by analyzing the phase difference between the stimulus and the CCEP responses, analogous to our previous research with single unit (1). We created a network model with SFA and STD by extending previous modeling (2,3). We investigated the interaction between SFA and STD using spectral analysis and their stabilizing properties by computing the largest Lyapunov exponent over a range of connectivities.
Results
Across participants, the cortical evoked response showed phase advances of approximately 5–30 degrees across modulation frequencies, consistent with adaptation on multiple timescales. These phase leads appear to be more pronounced in the clinically identified seizure onset zone, suggesting that compensatory negative feedback inhibition is upregulated. A phase lead at a particular frequency is consistent with adaptation (or dampening) at that timescale (2). Our network models showed a nonlinear interaction between SFA and STD, similar to other models (3), which may help maintain a homeostatic level of activity. Further, we found SFA and STD stabilized a wide range of networks onto the edge of chaos.
Discussion
Results suggest that neural mechanisms of feedback inhibition may be assessed at the level of EEG using stimulation-based methods, like sine-modulated CCEPs, or passive methods, such as by comparing changes in spectrograms. We find evidence of multiple timescale adaptation at the level of CCEPs, which may be one way the brain maintains stability. Our computational model suggests that SFA and STD can dynamically rebalance a wide range of networks and that these kinds of mechanism may result in telltale signs on spectrograms.
Acknowledgements
Work was supported by NINDS R01NS129622.
References
References
1.Lundstrom, B. N., Higgs, M. H., Spain, W. J., Fairhall, A. L. (2008). Fractional differentiation by neocortical pyramidal neurons. Nat Neurosci.https://doi.org/10.1038/nn.2212
2.Lundstrom, B. N. (2015). Modeling multiple time scale firing rate adaptation in a neural network of local field potentials. Journal of Comp Neurosci. https://doi.org/10.1007/s10827-014-0536-2
3.Lundstrom, B. N., Richner, T. J. (2023). Neural adaptation and fractional dynamics as a window to underlying neural excitability. PLOS Comp Bio. https://doi.org/10.1371/journal.pcbi.1010527
